Dynamical systems describing whole cells are on the verge of becoming a reality. But as
models of reality, they are only useful if we have realistic parameters for the molecular …

Agent-based modelling has proven its usefulness in several biomedical projects by
explaining and uncovering mechanisms in diseases. Nevertheless, the scenarios addressed …

Mechanistic models are essential to deepen the understanding of complex diseases at the
molecular level. Nowadays, high-throughput molecular and phenotypic characterizations …

Likelihood-free methods are often required for inference in systems biology. While
approximate Bayesian computation (ABC) provides a theoretical solution, its practical …

Pathogens face varying microenvironments in vivo, but suitable experimental systems and
analysis tools to dissect how three-dimensional (3D) tissue environments impact pathogen …

Individualized anatomical information has been used as prior knowledge in Bayesian
inference paradigms of whole-brain network models. However, the actual sensitivity to such …

The key processes in biological and chemical systems are described by networks of
chemical reactions. From molecular biology to biotechnology applications, computational …

Mathematical models are increasingly designed to guide experiments in biology,
biotechnology, as well as to assist in medical decision making. They are in particular …

The Python package pyABC provides a framework for approximate Bayesian computation
(ABC), a likelihood-free parameter inference method popular in many research areas. At its …

The growth and dynamics of epithelial tissues govern many morphogenetic processes in
embryonic development. A recent quantitative transition in data acquisition, facilitated by …